Mixed Membership CNNs


Mixed Membership CNNs – We propose a flexible multivariate and univariate network-based approach to learn latent variables from noisy and noisy data. Our approach is trained using a CNN trained on a multi-dimensional representation of the data matrix. The CNN classifier is learned by applying a linear feature learning algorithm to the latent variable matrix. The data matrix is used as the latent variable vector and a kernel function is fed with the latent variable matrix as input. Experiments on two widely used datasets (the MNIST and CUHK) show that this robust CNN approach can learn the latent variables without significantly perturbing the data matrix.

In this work we present a novel recurrent neural network architecture, Residual network, for Image Residual Recognition (ResIST), which aims to learn latent features from unlabeled images, which is commonly used for training ResIST. The ResIST architecture was designed to be flexible to overcome the limitation of traditional ResIST architectures such as ResNet, by leveraging the deep latent representations to perform the inference task. We propose a novel architecture that learns the latent features according to its labels, based on an effective learning mechanism to improve the performance. On the other hand, it achieves the same performance without additional expensive training time. We experiment the ResIST architecture on three datasets, namely, MNIST, PASCAL VOC and ILSVRC 2017 ResIST dataset, and we obtain a novel competitive results.

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Mixed Membership CNNs

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  • A Multi-Task Algorithm for Predicting Player Profiles and their Predictions from Social Media

    Recurrent Residual Networks for Accurate Image Saliency DetectionIn this work we present a novel recurrent neural network architecture, Residual network, for Image Residual Recognition (ResIST), which aims to learn latent features from unlabeled images, which is commonly used for training ResIST. The ResIST architecture was designed to be flexible to overcome the limitation of traditional ResIST architectures such as ResNet, by leveraging the deep latent representations to perform the inference task. We propose a novel architecture that learns the latent features according to its labels, based on an effective learning mechanism to improve the performance. On the other hand, it achieves the same performance without additional expensive training time. We experiment the ResIST architecture on three datasets, namely, MNIST, PASCAL VOC and ILSVRC 2017 ResIST dataset, and we obtain a novel competitive results.


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